Modeling and Optimization of Copper Flash Smelting Process Based on Neural Network

<正>The copper flash smelting process neural network model（CFSPNNM）was developed,its input layer includes eight nodes:oxygen grade（OG）,oxygen volume per ton of concentrate（OVPTC）,flux rate（FR）and quantifies of Cu,S,Fe,SiO2 and MgO in copper concentrate;output layer includes three nodes:matte grade,matte temperature and Fe/SiO2 in slag,and net structure was 8-13-10-3.Then,the internal relationship between the technological parameters and the objective parameters was built after the CFSPNNM was trained by using GA-BP algorithm.Moreover,the technological parameters were optimized by using genetic algorithms（GA）to make energy consumption the lowest.Simulation results showed that the CFSPNNM had high prediction precision and good generalization performance.Compared with the practical average data,the energy consumption can be reduced by 6.8% if the smelting process is controlled by adopting the optimized technological parameters.